The goal of the Data Science area of study is to equip students with both practical tools and theoretical knowledge to make sense of large amounts of data. We will explore popular tools being used today (i.e., Python), deep-learning libraries, advanced probabilistic-reasoning tools and distributed-computation systems.
This area of study will integrate faculty expertise from Electrical and Computer Engineering, Computer Science and Computational Medicine.
The curriculum will focus on unifying statistics, data mining and analysis, machine learning, and distributed and parallel systems to understand and analyze large amounts of data. During the lectures, students will learn various theoretical and algorithmic computational models and theory. They will then use these tools to analyze real-world data for their projects.
“This is the era of big data. So much data is being generated from everywhere, and the knowledge and ability to leverage that data and make sense of it will be highly desirable by industry.”
Area Director: Prof. Guy Van den Broeck
|COM SCI M245 Big Data Analytics (Instructor: Prof. M. Sarrafzadeh)||
COM SCI 260C/EC ENGR C247 Deep Learning
(Instructors: Prof. C.J. Hsieh/Prof. Y. Cao)
|COM SCI 263
Natural Language Processing(Instructor: Prof. K. W. Chang)
|COM SCI 143
Data Management Systems(Instructor: Prof. J. Cho)
|COM SCI 247
Advanced Data Mining(Instructor: Prof. Y. Sun)
|Engineering Professional Development Elective||Engineering Professional Development Elective|
|Engineering Professional Development Elective|
|12 Units||8 Units||8 Units||8 Units|